Downscaling Future Temperature and Precipitation Values in Kombolcha Town, South Wollo in Ethiopia

Research Article Volume 5:4, 2021 Journal of Environmental Hazards

ISSN: 2684-4923 Open Access Downscaling Future Temperature and Precipitation Values in Kombolcha Town, South Wollo in Ethiopia

Kasye Shitu1* and Mengesha Tesfaw2 1Department of Soil Resource and Watershed Management, Assosa University, Assosa, Ethiopia 2Departments of Soil Resource and Watershed Management, Wolidia University, Wolidia, Ethiopia

Abstract Whilst climate change is already manifesting in Ethiopia through changes in temperature and rainfall, its magnitude is poorly studied at regional levels. Therefore, the main aim of this study was statistically downscale of future daily maximum temperature, daily minimum temperature, and precipitation value in Kombolcha Town, South Wollo, in Ethiopia. For this the long term historical climatic data were collected from Ethiopian National Meteorological Agency for Kombolcha station and the GCM data were downloaded from the global circulation models of, the Canadian Second Generation Earth System Model from the link (http://climate scenarios.canada.ca/?page=dstsdi). For future climate data generation among the different downscaling techniques, the statistical down scaling method, a type of regression model was used and the variations of temperature (maximum and minimum) and precipitation in the town for annually and seasonally condition were analysis based on the base of the 2020s, 2050s and 2080s. In the future, relative to the observed mean value of annual rainfall in Kombolcha town, mean value of annual rainfall will decrease 1.36% - 7.03% for RCP4.5and 5.37% -13.8% for RCP8.5 emission scenarios in the last 21 century. Both maximum and minimum temperature of the town will be increased in the future time interval for both RCP4.5 and RCP8.5 emission scenarios. The rise in temperature will exacerbate the town maximum heat effects in warm seasons and decrease in precipitation is expected along with a possible risk of water supply scarcity due to a low level of water supply access and a high rate of urbanization. Keywords: Downscaling • Kombolcha • Precipitation and temperature • Emission scenarios.

Introduction significantly increasing trends in the frequency of hot days, are much larger increasing trends in the frequency of hot nights. The average number of ‘hot’ days per year increased by 73 (an additional 20% of days) between 1960 Global climate has changed over the last century mainly, due to and 2003 [12]. In the country related with precipitation, consistent models’ anthropogenic factors [1]. Climate change is described as the most universal ensemble of different model, indicating increases in total precipitation occurring and irreversible environmental problem facing the planet Earth [2] and in ‘heavy’ events, and increases in the magnitude of one-day maxima and five- becoming one of the most threatening issues for the world today in terms of its day maxima rainfall [13]. global context and its response to environmental and socioeconomic drivers [3]. The Intergovernmental Panel on Climate Change (IPCC) in its Fourth Whilst climate change is already manifesting in Ethiopia through changes Assessment Report observed an increase of 0.74 ± 0.18°C in mean annual in temperature and rainfall, its magnitude is poorly studied at regional levels global temperature [1]. According to [4] most scholars have agreed that the [2]. This is because of that, the results of the temperature and precipitation global annual average temperature is likely to be 2°C above pre-industrial data that obtained from meteorological observation of some regions are levels by 2050. This may makes the world more intense rainfall, frequent and missing and limits the downscaling of future weather values from general intense droughts, floods, heat waves, and other extreme weather events. circulation models. Now a time we can determine the consequences of climate Africa is considered as the most vulnerable continent to climate change in change earlier and our self for necessary adaptation measures by simulate the world [5]. According to AR4, it is very likely that all of Africa will warm up changes in climatic elements of the present and future from global general during this century and that, throughout Africa and in all seasons, the warming circulation models (GCMs) [12]. However, due to, coarse resolution of the will be larger than the global annual mean warming [6]. According to future GCM output it is difficult to apply the raw data of GCMs at a local scale, such as projections, precipitation and temperature will increase over Eastern Africa in the watershed scale or points of emphasis of this study urban climate change the coming century [7]. It is predicted that the temperature in Africa continent without downscaling [14]. Therefore, in order to use the results of general will rise by 2 to 6°C over the next 100 years [8]. circulation model to study the impact of climate change on a local scale, it is common to downscale and bring the results into the finest resolution. This can Regionally, in East Africa, studies indicate that in countries like Burundi, be done through the development of tools for downscaling GCM predictions of Kenya, Sudan, and Tanzania people are badly hit by the impacts of climate climate change to regional and local or station scales [15]. chang [9,10]. Temperature projections in the region indicates that, the median near-surface temperature in the 2080 to 2099 period will increase by 3°C to The Statistical Downscaling Model (SDSM) is a freely available tool that 4°C when it compared to the 1980 to 1999 period. It has to be underlined produces high resolution climate change scenarios result from GCMS for local that this increase is about 1.5 times the projected global mean response [6]. scale climate change study [16]. The SDSM provides reasonably good results Because of significant dependence on the agricultural sector for production, after calibration with NCEP predictor variables [1] and establish a relationship employment, and export revenues, Ethiopia is seriously threatened by climate between past local climate and past GCM outputs and extend this relationship change, which contributes to frequent drought, flooding, and rising average into the future, to generate future local climate series from future GCM output temperatures [11]. In the country, the daily temperature observations show [17]. The projection of future climate value is critical information that is needed to assess the impact of potential climate change on human beings and on the *Address for Correspondence: Kasye Shitu, Department of Soil Resource and natural environment [18]. It is also helpful for long-term planning development Watershed Management, Assosa University, Assosa, Ethiopia; Tel: 910134821; at both regional and national levels for mitigation and adaptation strategies of E-mail: [email protected] future climate change as, it gives opens space for a set of potential responses [19]. Copyright: © 2021 Kasye Shitu, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits In Ethiopia, studies like, statistical downscaling for daily temperature and unrestricted use, distribution, and reproduction in any medium, provided the rainfall in South Wollo [20], the study of future changes in climate parameters in original author and source are credited. Amhara Regional State [2], future climate studies in northwestern Ethiopia for Received 11 May 2021; Accepted 29 June 2021; Published 07 July 2021 assessing the hydrological response of the Gilgel Abay River to climate change Shitu K, et al. J Environ Hazard, Volume, Volume 5:4, 2021 in the Lake Tana Basin [12], climate change impact on the Geba Catchment Materials and Methods in Northern Ethiopia [21], GIS based quantification and mapping of climate change vulnerability hotspots in Addis Ababa [14], Impacts of Climate Change Description of the study area Under CMIP5 RCP Scenarios on the Hydrology of Lake Ziway Catchment [22], Impact of Climate Change on Hydrology of the Upper Awash River Basin [23], The study was conducted in Kombolcha town, which is found to the North Impact of Climate Change on Evapotranspiration and Runoff in Awash basin East of Ethiopia in Amhara regional state of South Wollo Zone. The town is [24] and Climate Change Impact on Water Resources in the Awash Basin [25] located in a range of altitudes and longitude between11º1'30"- 11º15'0" N were conducted by using downscaling applications in different time intervals latitude and 39º40'30" - 39 º 49’30” E longitudes. It is one of the industrial throughout all direction of the country to detect climate change impacts in towns in Amhara regional state of Ethiopia. Based on the 2007 national agricultural and hydrological applications. But, applications of statistical census conducted by the central statistical agency of Ethiopia (CSA), downscaling of general circulation models for future time temperature and Kombolcha has a total population of 85,367 of whom 41,968 are men and precipitation value estimation for industrial town of Kombolicha, South Wollo in 43,399 women; 58,667 or 68.72% are urban inhabitants living in town of Ethiopia has not been undertaken. Kombolcha, the rest of the population is living at rural Kebles around the town (Figure 1). It is important to some large cities of East and North African [26,27] and Addis Ababa city in Ethiopia [14,18] use of downscaled results from GCMs to The town is a plain land with altitudes difference of 1,500 m - 1,840 m assess future projections and to identify adaptation measures were explored above sea level and surrounded by hills. It is crossed by Borkena River and recently. Only a few studies are available for Kombolcha town, which differ numerous small streams. The Borkena valley and the gullies formed by the in method and temporal scale from these studies [28] and [29]. So, in order streams from the surrounding hills have made the configuration of the town to fill the above listed limitation by establish a relationship between past undulating. The average annual maximum temperature for Kombolcha local climate and past GCM outputs and extend this relationship into the over the last 30 years (1976-2005) was 26.3ºC and the average annual future time interval required scientific evidence on statistically downscaling minimum temperature was 12.69ºC. Also in the last 30 years the average of future temperature and precipitation values since, it is vital for policymakers, annual rain fall for the town was 1021.6 mm. The monthly rainfall researcher, planner to formulate the adaptation and mitigation options of future distribution of town indicates that July and August are the wettest months temporal and spatial variation in the study area. Therefore, the main aim of of the year in the past time (1976-2005) (Figure 2); they get more than 250 this study was statistically downscale of future daily maximum temperature, mm of average monthly rainfall. Also, as shown in Figure 2, there is high daily minimum temperature, and precipitation value in Kombolcha Town, South variation between the daily maximum and minimum temperature of the Wollo, in Ethiopia. town in the time interval of 1976-2005.

Figure 1. Map of the study area (Source: Arc GIS 10.7).

350 35 a y )

300 30 a y ) d / d /

m 250 25 ℃ m ( ( e

200 20 r u o n i

150 15 a t r a t t e i

100 10 p p i m c e e 50 5 r T r

P 0 0 Jan Feb Mar Apr May JUN Jul Aug Sep Oct Nov De

Rainfall Tmax Tmin Figure 2. Observed monthly mean rainfalls, Tmax and Tmin of the study area (1976-2005).

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Materials, Software and Models to be Downscaling Method used Climate scenarios data from a global climate model (GCM) are usually at a large scale, for this mater, downscaling is mandatory for impact and Different materials, softwares and models were used in adaptation studies as they require detailed local data. Among the different this research. downscaling techniques, the SDSM a type of regression model was used in this study. Because a number of studies indicate that the SDSM yields reliable Software like: Arc GISversion 10.7.1 to locate the study area, PCP estimates of extreme temperatures, seasonal precipitation, totals and inter site INSTAT to covert the long term daily data in to monthly and annual base precipitation behaviors for a given study area [14,21] in addition to this SDSM time interval XLSTAT for homogeneity and outlier data test for the observed is widely applied in many regions of the world over a range of different meteorological data, Mendeley Desktop application for reference citation from climatic condition and permits the spatial downscaling of daily predictor– different sources. predictand relationships using multiple linear regression techniques (Yihun et al., 2013). Models like: Climate model (canESM2) to generate the base time and future time climate scenario data for RCP4.5 and RCP8.5 emission scenario. SDSM used SDSM4.2.9a decision support tool which was downloaded from https://www.sdsm.org.uk/for the assessment of regional climate change Data description impacts. This decision support tool was developed by [16] and used to Historical meteorological variables such as rainfall, maximum and downscale large predictors. The SDSM software reduces the task of statistically minimum temperature were required as predicant to downscale the respective downscaling daily weather series into, quality control, data transformation, global GCM data statistically to the local climate variables. So, the long screening of predictor variables, model calibration, weather generation (using term (1976‑2005) historical climatic data were collected from the Ethiopian observed predictors), statistical analyses, graphing model output, and scenario National Meteorological Agency (NMA) only for Kombolcha station. Because, generation (using climate model predictors). in the town there is no other meteorological stations with temperature and Selection of predictors precipitation records. From the 30 years data of the station (1976-2005), 20 years data (1976–1995) were used for model calibration and the remaining 10 Selecting a predictor is an important step in the downscaling process. It is years data (1996–2005) were used for model validation. an iterative procedure consisting of a rough screening of the possible settings and predictors, which is repeated until an objective function is optimized [32]. The GCM Data which were required to project and quantify the future In this, screen variable operation was done after a quality control check and value of rainfall and temperatures in the study area and to calculate the transformation of rainfall data by fourth root. Identifying the best predictors relative change of these climate variables variables between the current and was conducted on the relationships which were drawn between a suite of the future time horizon was downloaded from the global circulation models of, global scale predictors and local scale predictand, based on linear correlation the Canadian Second Generation Earth System Model (canEMS2) from the analysis and scatter plots; and finally the predictors with the highest correlation link (http://climate scenarios.canada.ca/?page=dstsdi) which is freely available were selected. for the African window. From this GCM (CanEMS2) future scenario weather data for the ground stations were generated by using statistical downscaling From Canadian climate change scenario group website (http://climate method (SDSM) for the two emission scenarios (RCP 4.5 and RCP8.5) from scenarios.canada.ca/?page=dstsdi) for the African window, the National 2006 -2100 based on the means of the 20 ensembles of SDSM. The base Centers for Environmental Predictions (NCEP 1961–2005) reanalysis data time scenario for both RCP4.5 and RCP8.5 emission scenarios also generate and canESM2 predictor variables for RCP4.5 and RCP 8.5 are obtained on for the evaluation of the model performance by comparing the mean value of a grid by grid-box basis from a resolution of 2.8° latitude and 2.8° longitude. observed and base time weather parameter in monthly and annual base. So, the required predictor data that represent the town were down loaded from Kombolcha station and the determination of empirical relationships Data preparation between gridded predictors (such as mean sea-level pressure) and single-site The climate data that were collected from their respective source for model predictands (such as station precipitation) was performed for all parameters input were first captured with Microsoft excel 2007 spreadsheet following the (rainfall, maximum temperature and minimum temperature). The predictor data day of the year entry formula; second the data were checked for the missing files downloaded from the grid of interest (station of the study area) consists of value, outlier, consistency, homogeneity and finally, by using INSTAT plus the following three directories [16]. software, the daily data were summarized into annual, monthly and seasonal (a) NCEP_1961-2005: This directory contains 45 years of daily observed time scale based on the objective this study. predictor data, derived from the NCEP reanalysis, normalized over the Statistical check about the outlier value determination of meteorological complete 1961-1990 period. data was conducted using Grubbs outlier data test in statistical software of (b) RCP4. _2006-2100: This directory contains 95 years of daily GCM XLSTAT. The data in the time interval were tested and the presence of outlier predictor data, derived from the canESM2 experiment, normalized over the in the data set was determined when p-value of the test was greater than 1961-1990 period. significant level of 0.05. In addition to this, Z-score was also used to dictate the outlier data by displayed the Z-score value in bold weather it is maximum or (c) RCP8.5_2006-2100: This directory contains 95 years of daily GCM minimum. After this data was arranged in normal data level by optimized this predictor data, derived from the canESM2 experiment, normalized over the outlier data sets, because most statisticians would agree that outliers should 1961-1990 period. not be removed automatically rather than they should be carefully studied [30]. Large-scale predictor variable information from National Center for Standard Normal Homogeneity Test (SNHT) from XLSTA statistical Environmental Prediction (NCEP_1961-2005) reanalysis data set is used software was used for homogeneity test of meteorological data. Because, for the calibration and validation of SDSM with the observed precipitation, SNHT is one of the most popular homogeneity tests in climate studies and maximum and minimum temperature data of this study; and also RCP 4.5 the test supposes that tested values are independent and identically normally and RCP8.5 _1961-2100 data were used for the baseline and future time distributed (null hypothesis) and alternative hypothesis assumes that the climate scenario generation of the station by using linear correlation and series has a jump-like shift or break [30] and results was determined by the scatter plot partial correlation analysis for appropriate downscaling predictor compaction of the significance level of alpha value (0.05 or 0.01) with the variables selection through the screen variable option of SDSM. This statistic computed p-value with the null hypothesis (Ho) and alternative hypothesis identifies the amount of explanatory power of the predictor to explain the (Ha);When the computed p-value greater than the alpha valve the data is predictand [16]. For this, first, all the predictors Table 1 from historical records homogenous [31]. were correlated with the past observed (1976-2005) maximum temperature,

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Table 1. Twenty-six Predictor variables derived from African window for the study area. Predictor Variable Description Predictor Variable Description Ncepmslpgl.dat Mean sea level pressure Ncepp5zhgl.dat 500hpa divergence Ncepp1_fgl.dat Surface air flow strength Ncepp_fgl.dat 850 hpa air flow strength Ncepp1_ugl.dat Surface zonal velocity Ncepp_ugl.dat 850 hpa zonal velocity Ncepp1_vgl.dat Surface Meridians velocity Ncepp_vgl.dat 850 hpa meridians velocity Ncepp1_zgl.dat Surface velocity Ncepp_zgl.dat 850 hpa vortices Ncepp1thgl.dat Surface wind direction Ncepp850gl.dat 850 hpa geo potential height Ncepp1zhgl.dat Surface divergence Ncepp8thgl.dat 850 hpa wind direction Ncepp5_fgl.dat 500hpa air flow strength Ncepp8zhgl.dat 850 hpa divergence Ncepp5_ugl. Dat 500 hpa zonal velocity Ncepprcpgl.dat Relative humidity at 500 hpa Ncepp5_vgl.dat 500 hpa meridians velocity Nceps500gl.dat Specific humidity at 500hpa Ncepp5_zgl. Dat 500 hpa vortices Nceps850gl.dat Specific humidity at 850 hpa Ncepp500gl. Dat 500hpa geo potential height Ncepshumgl.dat Surface specific humidity Ncepp5thgl.dat 500hpa wind direction Nceptempgl.dat Mean temperature at 2m minimum temperature, and precipitation. Then, the predictors with the highest Results and Discussion correlation(r) and zero p-values were selected for each weather parameters. Finally, for the selection of the best predictor variables for each predictand Input data quality control (rainfall, maximum and minimum temperature) scatter plot was carried out in screen variable option of SDSM software. The meteorological data sets (rainfall, maximum and minimum Calibration and weather Generation of SDSM temperature) in the study area contend a total of 0.3-0.5% missing data from the whole observed data of rainfall and temperature, which were used in this The SDSM was calibrated for each parameters for the first 20 years (1976- study. Since the missing data are too small, the values were filled by averaging 1995) of observed data and predictor variables of NCEP reanalysis data sets of previous and following days of records [33,34]. and the last 10 years (1996-2005) of observed data from Kombolcha station Homogeneity of meteorological data was tested by Standard Normal were used for evaluation (weather generation) of the calibration data. Similar Homogeneity Test (SNHT) XLSTA of statistical software. Since p-values of to calibration of the model, weather generation was carried out from predictors the test were greater than the alpha value for both significance levels (0.01 of NCEP reanalysis datasets using independent observed data in a time series and 0.05), the data were considered as homogeneous in the time series of (observed data which was not used for calibration). observed data. The homogeneity test result for Kombolacha meteorological station indicated in Table 2. Statistical Downscaling Model (SDSM) can be simulated on monthly, seasonal and annual basis, but, for this study, the model was simulated Selection of downscaling predictor variables at monthly level by the process of conditional for rainfall parameter and unconditional for temperature parameter in order to see the monthly variation The number of selected predictors for all weather parameters (rainfall, of precipitation, maximum and minimum temperature values. According maximum and minimum temperature) varies from four to five in the study area (Table 2). The correlation (R) statistics and p-values were used to to [1], during calibration and validation of SDSM, direct link are established explain the strength of the relationship between the predictor and predicted. between predictors and predictands under unconditional model process and in So, in this study, Predictor variables which have a high correlation conditional models there is an intermediate process between regional forcing value(R) and zero p-values were selected. Even there were common and local weather. As a result, in conditional process the observed data were predictors for all weather parameter; in most cases the valve of correlation transformed by the fourth root before calibration and validations were done, (R) was different from parameter to parameter. The results also show but for unconditional process the observed data were used directly for model a strong association between mean temperatures at 2m (Nceptempgl.dat) for calibration and validation without transforming the data. all weather parameters in most case. In addition to this, as the results provide The performance of the model during model calibration and validation in Table 3 the amount of explanatory power (partial correlation R valve)for was measured by using statistics like mean, variance, sum, minimum and each predictor is unique. maximum values of simulated and observed data by summary statistics In the town, the selected predictors have better chance of the association analysis operation of SDSM software before scenario generation. This was with all predicand as the values of p is zero and Predictor like 500hpa used to select an appropriate event threshold value, variance inflation and bias geo potential height (Ncep500gl.dat) with maximum temperature, mean correction of the model. In addition to the above listed statistics, coefficient of temperature at 2m (Nceptempgl.dat) with minimum temperature and 850 determination (R2) for simulated and observed data was used for performance hpa geo potential height (Ncepp850gl.dat) with rainfall has higher positive measure of the model. However, for discussion coefficient of determination association. On the other hand, surface air flow strength (Ncepp1-fgl.dat) (R2) and mean of observed and simulated data comparison of all parameters predictor has higher negative association with both maximum and minimum were selected and the condition for each predictand (rainfall, maximum and temperature and there is no, any predictor which has negative association with minimum temperature) was presented in the following section. rain fall in the town (Table 3). Climate projection for future period Model calibration, weather generation and its performance Based on the objective of this study, to see the future temperature and precipitation values for Kombolcha town, the future data of these climate Before future scenario generation the results of the observed data for variables were downloaded from CanESM2 for Kombolcha meteorological maximum temperature, Minimum temperature and precipitation are correlated station. The future data were also generated for RCP 4.5 and RCP 8.5 with the modeled data during the calibration and validation periods using emission scenarios by SDSM method based on the average of 20 ensembles. the coefficients of determination. SDSM was calibrated for each parameters The variations of temperature (maximum and minimum) and precipitation in separately for the first 20 years (1976-1995) of observed data and predictor the town for annually and seasonally condition were analysis based on the variables of NCEP reanalysis data sets, weather generation was done for the base of the 2020s, 2050s and 2080s. last 10 years (1996 - 2005) of observed data set; the mean result of simulated

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Table 2. SNHT for meteorological data of Kombolcha station.

Alpha( ) Parameters Minimum Maximum Mean SD 0.05pvalue 0.01pvalue Rainfall 749.1 1319.3 1021.6 148.13 0.325 0.333 Maxi T 25.44 27.6 26.3 0.529 0.775 0.775 Min T 11.75 13.54 12.659 0.497 0.105 0.109

Table 3. Selected predictor variables in Kombolcha town. Predictand Predictor MaxT R2 P Tmin R2 P Rainfall R2 P Ncepp1-fgl.dat -0.277 0 -0.353 0 0.077 0 Ncepp1-vgl.dat -0.118 0 Ncep500gl.dat 0.295 0 0.203 0 0.072 0 Nceps500gl.dat 0.086 0 Ncepp8thgl.dat -0.212 0 Ncepp850gl.dat -0.11 0 0.249 0 Ncepshumgl.dat 0.369 0 0.096 0 Nceptempgl.dat 0.568 0 and observed data of all predictand data variations were determined. Figure In the future, relative to the observed mean value of Kiremt rainfall in 3a shows the mean of simulated and observed precipitation data of the study Kombolcha town, mean value of Kiremt rainfall will decrease for both RCP4.5 area from Kombolcha meteorological station. All most all months of the year and RCP8.5 emission scenarios in all three-time horizons (Table 5). The have underestimated simulated results for precipitation. In the model, even maximum decreasing rate will occur in 2050s for both RCP4.5 (16.75%) the above variation was observed, in related with a mean of observed and and RCP8.5 (26.7%). The decreasing rate of mean Kiremt season rainfall is mean of simulated rainfall data at the monthly level but, the value of coefficient higher for RCP8.5 relative to RCP4.5 in respective future time interval of this of determination (R2) which shows the overall association of observed and study. As cited by Belay et al.(2013), Arndt et al. (2011) indicate that the future simulated data was 0.929 for the study area. So, the overall agreements of projections Kiremt rainfall will decline by 20% in Ethiopia and also indicated that the observed and simulated mean results were very good with, related to the seasonally maximum precipitation reduction is projected during the Ethiopian coefficient of determination (R2). Figure 3b shows R2 value of Kombolcha local rainy season of Kiremt in the future time horizons. station in the study area. This show high variation and good agreement of Future expected mean annual rainfall in 2080s in Kombolcha town will observed data and simulated data from SDSM. The mean of observed and show maximum decreased (13.8%) mean annual rainfall value relative to simulated maximum temperatures in the study area show a little variation in observed mean annual rainfall (Table 6). In the town RCP4.5 shows 7.03% monthly levels. As shown in Figure 4a, the simulated maximum temperature and 1.36% decrement in the 2020s and 2050s respectively; RCP 8.5 also mean graph superimposed on observed maximum mean temperature graph, shows 5.47% and 7.22% decrement in the same respective time. This result this may be due the condition that the simulated and observed mean maximum is found in the same line with Asore et al. (2010), all models and scenarios temperature have all most equal means. In addition to this point, R2 values show a decrease in mean annual precipitation and consequently, a decrease show good relationship between the observed and simulated data Figure 4b. in runoff in lower Awash River Basin, where the area of this study found; The The minimum temperatures of the town have almost equal mean result Projection of future mean annual rainfall conditions suggest that the annual for observed and simulated conditions at the baseline time throughout all mean rainfall in the Central Rift Valley area is most likely to decrease (Belayet month of the years as indicated in Figure 5a. As a result of this graph of al., 2013), the generated future scenarios results show decreasing trend for simulated and observed minimum mean temperature in the baseline period mean annual precipitation in upper Awash Sub-basin, of Ethiopia [4] and also are superimposed each other in all months of the year. At the same time, as cited by [21], Monireh et al. (2013) was also indicated up to 25% decline of R2 value shows the presence of great conformity between the observed and mean annual precipitation in Ethiopia for the future time interval. simulated mean minimum temperature as indicated in Figure 5b. In this study Future temperature: Mean maximum temperature value which expected from the three weather parameters (rainfall, maximum and minimum tempera), in the future three-time horizons (2020s, 250s and 2080s) for the two emission the SDSM model simulation result shows good performance for maximum scenarios (RCP4.5 and RCP8.5) in Kombolcha town for Belg, Kiremt and temperature next to the minimum temperature when it evaluates by coefficient annual season are given in Table 7. In the town throughout the three-time of determination. horizons mean maxim temperature of the three seasons (Belg, Kiremit and Future temperature and precipitation annul) for both RCP4.5 and RCP8.5 emission scenarios indicates increment value when it compares with the base time mean maximum temperature. Future precipitation: The mean results of future precipitation, which was downscaled from canESM2 GCM by using NCEP-NCAR predictors show In the town the maximum increment of Belg season mean maximum significant variation when it compared with the reference period (1976-2005) temperature will be occurred in 2050s for both RCP4.5 and RCP8.5 with a value mean rainfall of the town for all rainfall season (annual, Kiremt and Belg). of 1.88ºC and 1.77ºC respactively. For Kiremt season this will be occurred in 2020s for both RCP4.5 (1.52ºC) and RCP8.5 (1.48ºC). In annual base even The mean value of rainfall for the Belge rain season for all three future time the increment will be occurred in 2020s for both RCP4.5 and RCP8.5, the horizons of this study Table 4 will indicate a higher value than the base time value is small when it compares with the Belg and Kiremt season increment mean value of Belg season rainfall for RCP4.5 emission scenario. However, values. for RCP8.5 emission scenario the mean value of Belg rainfall will indict lower value as compared to observed time mean Belg rainfall value. The maximum The mean annual, Kiremt and Belg minimum temperature value of increasing and decreasing mean value of Belg rainfall will occur in 2020s for Kombolcha town for the time of the 2020s, 2050s and 2080s are indicated RCP4.5 and RCP8.5 respectively (Table 4). Maximum increase and decrease in Table 8. The expected mean annual, Kiremt and Belg season minimum will occur by 2020s, for both RCP4.5 and RCP8.5 respectively 2020s = 2006 temperature of the town show increment values when it compares to base time -2035, 2050s = 2036 – 2065 and 2080s = 2066 – 2095. value of respective seasons.

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1.2 observed Simulated y = 0.8231x - 0.0216

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Figure 3. Simulated and observed mean daily precipitation (a) and (R2) in studyarea.

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40.00 ℃ ) ( R² = 0.9857 m ℃ 14.00

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J 0.00 5.00 10.00 15.00 20.00 J O c A p F S D e A u N M a M M a Time(Month) Simulated minimum Temp(℃) (a) (b)

Figure 5. Simulated and observed mean daily minimum temperature (a) and R2 (b) of the study area.

Table 4. Mean of future Belg season rainfall relative to the base period. Observation Projected Belg rainfall(mm) RCP4.5 RCP8.5 Base period 2020s 2050s 2080s 2020s 2050s 2080s 215.69 231.9 221.6 221 195.1 198.4 198.9 Increase/Decrease (%) 7.5 2.75 2.59 -9.57 -8.04 -7.78 ∗ ∗

Table 5. Futures mean Kiremt rainfall change relative to the base period rainfall.

Projected Annual rainfall(mm) Observation RCP4.5 RCP8.5 Base period 2020s 2050s 2080s 2020s 2050s 2080s 1023.43 951.5 1009.5 968.5 967.5 949.5 882.2 Increase/Decrease (%) -7.03 -1.36 -5.37 -5.47 -7.22 -13.8

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Table 6. Future annual rain fall change relative the base time. Projected Annual rainfall(mm) Observation RCP4.5 RCP8.5 Base period 2020s 2050s 2080s 2020s 2050s 2080s 1023.43 951.5 1009.5 968.5 967.5 949.5 882.2 Increase/Decrease (%) -7.03 -1.36 -5.37 -5.47 -7.22 -13.8

Table 7. Future annual, Kiremt and Belg season mean maximum temperature. Maximum Temperature ( ) Observation RCP4.5 RCP8.5 ℃ Base period 2020s 2050s 2080s 2020s 2050s 2080s Belg(27.87 ) 27.93 29.75 28.19 27.93 29.64 28.26 Difference 0.06 1.88 0.32 0.05 1.77 0.39 ℃ ⃰ ⃰ Kiremt(28.10 ) 29.62 29.2 28.67 29.58 29.29 28.26 Annual (26.86 ) 26.97 26.86 26.94 26.97 26.9 27.12 ℃ Difference 0.11 0 0.08 0.11 0.04 0.26 ℃

Table 8. Future annual, Kiremt and Belg season mean minimum temperature. Minimum Temperature Observation RCP4.5 RCP8.5 Base period 2020s 2050s 2080s 2020s 2050s 2080s Belg(13.72 ) 14.03 13.87 14.23 14.71 14.54 15.06 Difference 0.61 0.15 0.51 0.99 0.82 1.34 ℃ ⃰ ⃰ Kiremt(14.90 ) 15.55 15.29 16.51 16.19 15.07 15.63 Difference 0.65 0.39 1.61 1.29 0.17 0.73 ℃ ⃰ ⃰ Annual (12.63 ) 12.76 12.96 13.89 14.48 13.82 14 Difference 0.13 0.33 1.35 1.85 1.19 1.37 ℃ ⃰ ⃰

The future mean annual minimum temperature of the town will be increased elements of the present and future, which helps us to determine consequences by 1.35ºC for RCP4.5 and 1.37ºC for RCP8.5 in 2080s when it compared earlier and prepare for necessary adaptation measures. to the historical mean annual minimum temperature of the town. This is In Ethiopia, various downscaling applications and their potential to detect the maximum increment values in the future three-time horizon. Similar climate change impacts in agricultural and hydrological applications were nature with future mean annual minimum temperature in2080s, Belge conducted in different time intervals throughout all direction of the country. and Kiremt season mean minimum temperature in the 2080s expected by But, applications of statistical downscaling of general circulation models for the increment value for both RCP4.5 and RCP8.5 emission scenarios. In Belge the increment value for RCP8.5 (1.34ºC) will be higher than that of future time temperature and precipitation value estimation for industrial town RCP4.5 (0.51ºC); the condition will be reverse in Kiremt when the mean of Kombolicha, South Wollo in Ethiopia has not been undertaken. So, in order minimum temperature will be increased by 1.61ºC and 0.73ºC for RCP4.5 to fill the limitation by establish a relationship between past local climate and and RCP8.5 respectively. past GCM outputs and extend this relationship into the future time interval required scientific evidence on statistically downscaling of future temperature In the future three-time horizons of this study the maximum Belg season and precipitation values since, it is vital for policymakers, researcher, planner mean minimum temperature increment will be occurred in 2020s for RCP4.5 to formulate the adaptation and mitigation options of future temporal and and in 2080s for RCP8.5. For Kiremt season mean minimum temperature with spatial variation in the study area. Therefore, the main aim of this study was contradiction of Belg season mean minimum temperature, the increment will statistically downscale of future daily maximum temperature, daily minimum be occurred in 2080s for RCP4.5 and in 2020s for RCP8.5. temperature, and precipitation value in Kombolcha Town, South Wollo, in Ethiopia. Conclusion The mean value of rainfall for the Belge rain season for all three future time horizons of this study will indicate a higher value than the base time Africa is considered as the most vulnerable continent to climate change mean value of Belg season rainfall for RCP4.5 emission scenario. In the in the world. Regionally, in east Africa countries like Burundi, Kenya, Sudan, future, relative to the observed mean value of Kiremt rainfall in Kombolcha and Tanzania people are badly hit by the impacts of climate change. Because town, the mean value of Kiremt rainfall will decrease for both RCP4.5and of its significant dependence on the agricultural sector for production, RCP8.5 emission scenarios in all three-time horizons. Also, in the town employment, and export revenues, Ethiopia also seriously threatened by both RCP4.5 and RCP8.5 scenarios show a decrease in mean annual climate change, which contributes to frequent drought, flooding, and rising precipitation values throughout the three future time interval of this study. average temperatures. In the town throughout the three-time horizons both mean maxim and Whilst climate change is already manifesting in Ethiopia through changes minimum temperature of the three seasons (Belg, Kiremit and annul) for in temperature and rainfall, its magnitude poorly studied at regional levels. This both RCP4.5 and RCP8.5 emission scenarios indicates increment value is because of that, the results of the temperature and precipitation data that when it compares with the base time mean maximum temperature. The obtained from meteorological observation of some regions are missing and rise in temperature will intensify the town maximum heat effects in warm limits the downscaling of future weather values from general circulation models. seasons and decrease in precipitation is expected along with a possible One of the recent advances in climate science research is the development risk of water supply scarcity due to a low level of water supply access and of global general circulation models (GCMs) to simulate changes in climatic a high rate of urbanization in the town.

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